In “Data-Oriented Python Programming and Debugging,” you will develop Python debugging skills and learn best practices, helping you become a better data-oriented programmer. Courses in the series will explore how to write and debug code, as well as manipulate and analyze data using Python’s NumPy, pandas, and SciPy libraries. You’ll rely on the OILER framework – Orient, Investigate, Locate, Experiment, and Reflect – to systematically approach debugging and ensure your code is readable and reproducible, ensuring you produce high-quality code in all of your projects. The series concludes with a capstone project, where you’ll use these skills to debug and analyze a real-world data set, showcasing your skills in data manipulation, statistical analysis, and scientific computing.
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Data-Oriented Python Programming and Debugging Specialization
Write and systematically debug Python code. Develop readable and reproducible Python code while investigating, manipulating, and analyzing real-world data using Python libraries.
Instructors: Anthony Whyte
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What you'll learn
Effective use of modules, functions, and object methods in data-driven computing.
Competent independent debugging and self-help skills in Python.
Proficient programming with common data structures such as arrays and DataFrames using libraries like NumPy and pandas.
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Skills you'll gain
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What’s included
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Specialization - 4 course series
What you'll learn
Use Jupyter Notebook to implement basic Python workflows and constructs.
Apply the OILER framework for debugging many common Python bugs.
Use official Python documentation to enhance understanding of different programming formats.
Interpret Python error messages to resolve runtime execution issues.
Skills you'll gain
What you'll learn
Create and manipulate NumPy arrays, including performing basic arithmetic operations and handling missing data.
Apply advanced NumPy techniques such as broadcasting, masking, and aggregation functions.
Construct and modify pandas DataFrames and Series, use methods to filter and inspect data, and handle missing data.
Utilize pandas for data aggregation, summary statistics, and dataframe merging to analyze a real dataset.
Skills you'll gain
What you'll learn
Use vector operations in NumPy for applied mathematics.
Visualize and analyze data distributions using NumPy and SciPy.
Use statistics to describe patterns in data distributions.
Conduct statistical inference using hypothesis testing with computational methods.
Skills you'll gain
What you'll learn
Independently debug a variety of code issues.
Interpret and implement evolving project requirements.
Import, clean, and manipulate data acquired from remote sources.
Deliver notebooks that can be read, run, and reproduced.
Skills you'll gain
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Frequently asked questions
16 weeks, 11.25 hours per week (180 hours)
Learners should complete "Python 3 Programming" on Coursera or have equivalent experience with Python programming basics.
It is strongly recommended to take the courses in the order provided.
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Financial aid available,